1. Field of the Invention
The present invention relates to image processing. In particular, this invention relates to a method and apparatus for unsupervised learning of discriminative edge measures for vehicle matching between images from non-overlapping cameras.
2. Description of the Related Art
Visual object recognition is an important component of image processing for matching a movable object between two non-overlapping cameras, and prior research and development has provided a number of mechanisms for performing such visual object recognition. For example, visual object recognition may be determined as a function of (i) edge-based object matching, and (ii) learning robust and discriminative measures for classification.
Object matching using edge features has proven to be reliable. For example, edge features have been used to detect traffic signs and pedestrians, and even recognize hand gestures. Examples of prior edge-based match measures include Chamfer distance, Hausdorff distance, and Earth Mover's distance. In addition, both edge locations and edge orientations may be used to define a combined edge measure, which may be used to improve performance of the matching and classification. A SIFT descriptor may use aggregated measures computed from both gradient orientation and magnitude so as to tolerate slight location errors.
Despite the prior research and development, two issues related to edge-based measures exist. These issues include robustness and feature selection and combination. Many prior works have disclosed using clean edge maps for at least one of two edge maps. Truncated Chamfer distance or robust Hausdorff distance, for instance, may work for when one edge map is clean, but not when both edge maps are not clean.
The issues of feature selection and combination of discriminative edge measures focus on maximizing the overall classification performance. To address this, others have used learning discriminative image features with a limited set of labeled data based on a semi-supervised learning approach. In addition, others have addressed on-line selection of discriminative color features for tracking. In this case, learning is based on a set of foreground pixels and background pixels labeled by the tracker with a “center-surround” approach. However, a result can be biased by pixels that are incorrectly labeled.
Therefore, there is a need in the art for a method and apparatus that provides for unsupervised learning of discriminative edge measure for vehicle matching between non-overlapping cameras that is unsupervised and does not involve a fixed label set.